CN116031604B - Automatic debugging method of microwave filter based on response feature extraction - Google Patents
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Abstract
The invention discloses a microwave filter automatic debugging method based on response feature extraction, which comprises the following steps: setting an S parameter target value of a filter to be debugged, wherein the S parameter target value comprises target poles of S11 and S21 and target zeros of S11 and S21; acquiring sampling values of S11 and S21 of the filter to be debugged and extracting a coupling matrix; extracting the current pole-zero of the S11 and the S21 and the gradient of the preset topological element of the matrix to the pole-zero according to the coupling elements of the coupling matrix; constructing a square loss function of the extracted zero poles and the preset zero poles, adding a constraint item of a preset topological element to construct an optimization target, and optimizing the optimization target to obtain a perturbation matrix; and applying debugging to a limited number of physically-adjustable structures according to the perturbation matrix. The invention is used for automatically or manually debugging the electrical performance of the microwave filter.
Description
Technical Field
The invention belongs to the technical field of filter debugging, and particularly relates to an automatic debugging method of a microwave filter based on response feature extraction.
Background
Microwave filters are very important in the entire wireless communication system, and are widely used in various wireless communication systems. The electrical performance of the microwave filter is often inconsistent with the expected performance due to the influence of non-ideal factors such as processing errors in the actual production process, so that the physically adjustable structure of the filter is required to be debugged before the microwave filter is used, the process is often manually finished by a debugger in the past, the manual debugging efficiency is lower, and the improvement of the productivity is limited.
The current common computer aided debugging method is realized based on a coupling matrix theory, the basic idea is that an S parameter expression of a filter is read from a vector network analyzer or simulation software by a computer, then a coupling matrix is extracted through a coupling matrix comprehensive algorithm, the extracted coupling matrix is compared with a target matrix, the debugging direction and the adjusting quantity are determined according to the corresponding relation between the physical property and the electromagnetic property of the filter, and finally a robot arm or a human operator is guided to finish debugging.
The debugging strategy based on the coupling matrix is high in efficiency, but the existing debugging strategy is built on the assumption that the physical structure is consistent with the preset template, and the filter is usually subjected to non-ideal factors such as temperature drift and parasitism in actual physics, the coupling matrix extracted by the existing method has parasitic parameters outside the preset topology, so that the gold template is invalid, the next debugging direction cannot be accurately obtained, meanwhile, the influence of mechanical errors on debugging is not considered in the existing scheme, the phenomenon of repeatedly debugging the same coupling structure often occurs, and the debugging efficiency is seriously influenced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides an automatic debugging method of a microwave filter based on response characteristic extraction, can overcome the influence of parasitic coupling and repeated debugging on a computer auxiliary debugging process, and improves the debugging efficiency.
The aim of the invention is achieved by the following technical scheme:
An automatic debugging method of a microwave filter based on response feature extraction, the method comprising:
Setting an S parameter target value of a filter to be debugged, wherein the S parameter target value comprises target poles of S11 and S21 and target zeros of S11 and S21;
acquiring sampling values of S11 and S21 of the filter to be debugged and extracting a coupling matrix;
extracting the current pole-zero of the S11 and the S21 and the gradient of the preset topological element of the matrix to the pole-zero according to the coupling elements of the coupling matrix;
constructing a square loss function of the extracted zero poles and the preset zero poles, adding a constraint item of a preset topological element to construct an optimization target, and optimizing the optimization target to obtain a perturbation matrix;
And applying debugging to a limited number of physically-adjustable structures according to the perturbation matrix.
Further, extracting the current pole-zero of S11 and S21 and the gradient of the preset topology element of the matrix to the pole-zero according to the coupling element of the coupling matrix specifically includes:
And obtaining the current zero poles of the S11 and the S21 by calculating the matrix beam generalized eigenvalue, and calculating the preset topological element by the generalized eigenvector corresponding to the current zero pole of the S11.
Further, the constructing the square loss function of the extracted zero pole and the preset zero pole, and adding the constraint item of the preset topological element to construct the optimization target specifically includes:
the following square loss function C 11 was constructed:
wherein z p,zr,zt is the pole zero of S11 and S21, And (3) withIs a corresponding target value;
The optimization objective of the joining constraint term construct is:
C＝C1+μ∑|Mi,j|；
where μ represents a constant, |m i,j | represents the absolute value of the preset topology element.
Further, the optimizing target to obtain the perturbation matrix specifically includes:
Calculating the derivative of the element M i,j of the topology (i, j) location with respect to the loss function C 1;
solving C=C 1+μ∑|Mi,j | through a quasi-Newton algorithm, solving an optimized coupling matrix M *, and then calculating a perturbation matrix delta M of the coupling matrix as
ΔM＝M*-M1。
Further, the applying the debug to the limited number of physically-tunable structures according to the perturbation matrix specifically includes:
and adjusting a physical adjustable structure corresponding to the non-zero position in the perturbation matrix according to the perturbation value of the perturbation matrix.
Further, the method further comprises:
After the debugging is finished, judging whether the electric performance of the filter meets engineering indexes, if so, finishing the debugging, and if not, giving out the target value of the S parameter again to continue the debugging.
On the other hand, the invention also provides a device for debugging the zero pole of the filter, which comprises:
The target value setting module is used for setting an S parameter target value of the filter to be debugged, wherein the S parameter target value comprises target poles of S11 and S21 and target zeros of S11 and S21;
The coupling matrix extraction module is used for acquiring sampling values of S11 and S21 of the filter to be debugged and extracting a coupling matrix;
The pole-zero and gradient acquisition module is used for extracting the current pole-zero of the S11 and the S21 and the gradient of the preset topological element pair pole of the matrix according to the coupling elements of the coupling matrix;
the perturbation matrix calculation module is used for constructing a square loss function of the extracted zero poles and the preset zero poles, adding a constraint item of the preset topological elements to construct an optimization target, and optimizing the optimization target to obtain a perturbation matrix;
and the debugging module is used for debugging the limited physical adjustable structures according to the perturbation matrix.
In another aspect, the present invention further provides a computer device, where the computer device includes a processor and a memory, where the memory stores a computer program, and the computer program is loaded and executed by the processor to implement any one of the above-mentioned automatic debugging methods of a microwave filter based on response feature extraction.
In another aspect, the present invention further provides a computer readable storage medium, where a computer program is stored, where the computer program is loaded and executed by a processor to implement any one of the above-mentioned automatic debugging methods of a microwave filter based on response feature extraction.
The invention has the beneficial effects that:
(1) The invention provides a debugging method aiming at zero poles, which can be used for efficiently debugging echo and out-of-band rejection indexes of a filter.
(2) The pole-zero extraction and approximation process of the coupling matrix is performed through the preset coupling elements, so that the influence of parasitic parameters on debugging can be effectively solved, and the debugging efficiency is improved.
(3) The invention provides a method for searching key coupling elements, realizes a debugging strategy of only regulating a few physical adjustable structures at a time, avoids repeated debugging of the physical adjustable structures which are already debugged, and effectively reduces the debugging time.
Drawings
FIG. 1 is a schematic flow chart of an automatic debugging method of a microwave filter based on response feature extraction provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of parasitic parameters in a coupling matrix according to an embodiment of the present invention;
FIG. 3 is a graph showing the S-parameter response when the order 7 filter of the present invention is not being debugged;
FIG. 4 is a graph showing the S-parameter response after the order 7 filter of the present invention is debugged;
fig. 5 is a block diagram of a filter pole-zero debugging device according to an embodiment of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The existing debugging strategies are all established on the assumption that the physical structure is consistent with a preset template, and the filter is usually subjected to non-ideal factors such as temperature drift and parasitism in actual physics, the coupling matrix extracted by the existing method has parasitic parameters outside the preset topology, so that the golden template is invalid, the next debugging direction cannot be accurately obtained, meanwhile, the influence of mechanical errors on debugging is not considered in the existing scheme, the phenomenon of repeatedly debugging the same coupling structure often occurs, and the debugging efficiency is seriously influenced.
In order to solve the above technical problems, the following embodiments of the automatic debugging method for a microwave filter based on response feature extraction of the present invention are proposed.
Example 1
Referring to fig. 1, as shown in fig. 1, a flow chart of a method for automatically debugging a microwave filter based on response feature extraction provided in this embodiment is shown, and the method specifically includes the following steps:
Step (1): for an N-order filter, a target value of a preset transfer function zero pole of the filter is firstly given, wherein the target poles including S11 and S21 are S11 is the target zero pointAnd S21 is the target zero point
As an implementation manner, the target value of the preset transfer function zero pole in this embodiment needs to be obtained from the actual measurement result of the successfully debugged filter, or obtained by a generalized chebyshev integrated algorithm.
In this embodiment, S11 and S21 refer to elements of the S parameter matrix, S11 represents reflection from 1 port, S21 represents transmission from 1 port to 2 ports, and they describe filtering performance of the filter.
Step (2): the coupling matrix of the current filter response is extracted. The specific method comprises the steps that a computer reads an S2P format file of a pre-debugging state from a vector network analyzer to obtain sampling values of S11 and S21, then carries out low-pass mapping on the sampling values, maps sampling frequencies to-1, converts the sampling values of S11 and S21 into mathematical expressions of S11 and S21 through a curve fitting algorithm, and extracts a coupling matrix M through a coupling matrix comprehensive algorithm according to the mathematical expressions of S11 and S21, wherein the M has parasitic coupling besides coupling with preset topology. Referring to fig. 2, a schematic diagram of parasitic parameters in the coupling matrix of the present embodiment is shown in fig. 2.
Step (3): according to the coupling matrix M extracted in the step (2), poles z p of S11 and S21 and zero z r of S11 and zero z t of S21 are calculated respectively through a QZ decomposition algorithm, and meanwhile, the gradient of an element M i,j of the matrix M at the position of a preset topology (i and j) on the poles zero is calculated.
Specifically, step (3) includes the sub-steps of:
step (3 a): constructing a loop matrix U and a loop matrix W as a coupling matrix M
U＝diag{0,1,1,...,1,0}
W＝R+jM，R＝diag{1,0,0,...,0,1}
The generalized eigenvalue lambda p of the matrix W to the matrix U and the corresponding generalized eigenvector x p are calculated by a QZ decomposition algorithm, and according to the coupling matrix theory, the generalized eigenvalue lambda p of the matrix W to the matrix U is equal to the pole z p of S11 and S21.
Step (3 b): according to the poles z p of S11 and S21 obtained in the step (3 a) and the corresponding generalized eigenvector x p, according to the matrix derivation rule, the gradient of the element M i,j of the matrix M at the position of the preset topology (i, j) to the pole z p can be obtainedIs that
Wherein x p,i and x p,j correspond to the ith and jth elements of the generalized eigenvector x p, respectively.
Step (3 c): the matrix W r is constructed as
Wr＝Rr+jM,Rr＝diag{-1,0,0,...,0,1}
According to the loop matrix U obtained by calculation in the step (3 b), the generalized eigenvalue lambda r of the matrix W r on the matrix U and the corresponding generalized eigenvector x r are calculated through a QZ decomposition algorithm, and according to the coupling matrix theory, the generalized eigenvalue lambda r of the matrix U and the matrix W r is equal to the zero point z r of S11. Meanwhile, according to a matrix derivation rule, the gradient of the element M i,j of the matrix M at the position of the preset topology (i, j) to the S11 zero z r can be obtainedIs that
Step (3 d): according to the loop matrix U and the matrix W obtained in the step (3 a), removing the first row and the first column of the matrix U and the matrix W to obtain a submatrix U t and a matrix W t, calculating a generalized eigenvalue lambda t and a corresponding generalized eigenvector x t of the matrix W t on the matrix U t through a QZ decomposition algorithm, and adopting a coupling matrix theory, wherein the generalized eigenvalue lambda t of the matrix W t on the matrix U t is equal to a zero point z t of S21. Meanwhile, according to a matrix derivation rule, calculating the gradient of an element M i,j of the matrix M at a preset topology (i, j) position to a zero point z t of S21Is that
Step (4): constructing the square loss function of the extracted zero pole and the preset zero pole, adding an L1 norm constraint term of a preset topological element to construct an optimization target, keeping parasitic coupling elements unchanged, optimizing the numerical value of the preset topological coupling through a quasi-Newton algorithm, and converging the coupling matrix M to the preset zero pole under the condition of minimum element change in the optimization process.
The zero z r of the coupling matrix is extracted, the zero z t of the S21 is matched with the zero z p of the S11 and the pole z p of the S21 is matched with the ideal zero of the S11Zero point of S21And poleMeanwhile, a constraint term mu sigma I M i,j I of a preset topological coupling M i,j L1 norm is added into the optimization target, the topological position and the perturbation value of the to-be-perturbation coupling are calculated through a quasi-Newton algorithm according to the gradient obtained through calculation in the step (2).
Specifically, step (4) includes the sub-steps of:
step (4 a): in order to preset the poles zero z p,zr and z t extracted in the steps (3 a) - (3 d) to the step (1) And (3) withApproximating, the following square loss function C 1 is constructed as
Step (4 b): in order to make the variation items of the preset topological coupling as few as possible, constructing an L1 norm constraint item C 2 of the preset topological coupling M i,j as
C2＝μ∑|Mi,j|；
Step (4 c): calculating the total loss function C as the loss function C of the step (4 a) and the step (4 b) according to the loss function C 1 and the constraint term C 2
C＝C1+μ∑|Mi,j|；
Where μ represents a constant, |m i,j | represents the absolute value of the preset topology element.
Step (4 d): according to the loss function C obtained in the step (4C), the derivative of the element M i,j of the preset topology (i, j) position calculated in the step (3 b) -step (3 d) on the zero-pole is utilized, and according to the chained derivative rule, the derivative of the element M i,j of the topology (i, j) position on the loss function C 1 is calculated as follows
Step (4 e): constructing a jacobian matrix according to gradient information obtained in the step (4 d), setting an error threshold epsilon and a maximum iteration step K for optimizing exit, taking a currently extracted coupling matrix M as an initial value, solving an objective function C in the step (4C) through a quasi-Newton algorithm to obtain an optimized coupling matrix M *, and calculating a perturbation matrix of the coupling matrix as an initial value
ΔM＝M*-M1；
The perturbation matrix Δm is a sparse matrix with only a small portion of non-zero terms.
By optimizing C, the change amount delta M i,j of the preset topological element M i,j and the change amount delta M of the coupling matrix can be obtained, wherein the change amount of the preset topological element is the numerical value change amount of the coupling matrix at the preset topological position. Meanwhile, constraint terms are added in the optimization target, so that only a small number of elements of the delta M are non-0, and therefore the delta M is a perturbation matrix.
Step (5): according to the perturbation matrix Δm calculated in step (4), a non-zero term of Δm is proposed, in the first embodiment, the coupling magnitude and the magnitude of the adjustment quantity are approximately in a linear relationship, the adjustment quantity can be determined by the ratio of Δm to sensitivity, and then the computer control robot is made to adjust the corresponding physical adjustable structure, and since Δm is sparse, only a few physical adjustable structures need to be adjusted. After the debugging is finished, judging whether the electric performance of the filter meets engineering indexes, if so, ending the debugging, and if not, turning to the step (1) to continue the debugging.
It should be noted that, in this embodiment, the perturbation matrix corresponds to the change amount of the coupling matrix, and only a few elements of the perturbation matrix are non-0. The influence of the 0 term can be ignored, only the non-0 term of the perturbation matrix is processed, the actual topological position corresponding to the non-0 term in the perturbation matrix is debugged, the corresponding adjustable structure is debugged when the actual topological position is reflected to the filter, and each topological element of the coupling matrix corresponds to one adjustable structure of the filter.
Referring to fig. 3 and 4, the S-parameter response curve when the 7-order filter is not being debugged is shown in fig. 3, and the S-parameter response curve after the 7-order filter is debugged is shown in fig. 4. It can be seen that the S11 after the completion of the debugging is below 20dB, namely, a good S parameter response curve can be obtained after the debugging by using the method.
Example 2
Referring to fig. 5, as shown in fig. 5, the structure block diagram of the pole-zero debugging device for a filter provided in this embodiment specifically includes the following structures:
the target value setting module is used for setting an S parameter target value of the filter to be debugged, wherein the S parameter target value comprises target poles of S11 and S21 and target zeros of S11 and S21;
The coupling matrix extraction module is used for acquiring sampling values of S11 and S21 of the filter to be debugged and extracting a coupling matrix;
The pole-zero and gradient acquisition module is used for extracting the current pole-zero of the S11 and S21 and the gradient of the preset topological element pair pole of the matrix according to the coupling elements of the coupling matrix;
The perturbation matrix calculation module is used for constructing the square loss function of the extracted zero poles and the preset zero poles, adding a constraint item of the preset topological elements to construct an optimization target, and optimizing the optimization target to obtain a perturbation matrix;
and the debugging module is used for applying debugging to the limited physical adjustable structures according to the perturbation matrix.
Example 3
The preferred embodiment provides a computer device, which can implement the steps in any embodiment of the method for automatically debugging a microwave filter based on response feature extraction provided by the embodiment of the present application, so that the beneficial effects of the method for automatically debugging a microwave filter based on response feature extraction provided by the embodiment of the present application can be achieved, which are detailed in the previous embodiments and are not described herein.
Example 4
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, an embodiment of the present invention provides a storage medium having stored therein a plurality of instructions capable of being loaded by a processor to perform the steps of any one of the embodiments of the method for automatically debugging a microwave filter based on response feature extraction provided by the embodiment of the present invention.
Wherein the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.
The instructions stored in the storage medium can execute the steps in any of the embodiments of the method for automatically debugging a microwave filter based on response feature extraction provided by the embodiments of the present invention, so that the beneficial effects that any of the methods for automatically debugging a microwave filter based on response feature extraction provided by the embodiments of the present invention can be achieved, which are detailed in the previous embodiments and are not described herein.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (6)
1. An automatic debugging method of a microwave filter based on response feature extraction is characterized by comprising the following steps:
Setting an S parameter target value of a filter to be debugged, wherein the S parameter target value comprises target poles of S11 and S21 and target zeros of S11 and S21;
acquiring sampling values of S11 and S21 of the filter to be debugged and extracting a coupling matrix;
Extracting the current pole-zero of the S11 and the S21 and the gradient of the preset topological element pair pole of the coupling matrix according to the coupling elements of the coupling matrix;
Constructing a square loss function of the extracted zero poles and a preset zero poles, adding a constraint item of a preset topological element to construct an optimization target, and optimizing the optimization target to obtain a perturbation matrix, wherein the preset zero poles are target values of zero poles of a preset transmission function of the filter;
And applying debugging to a limited number of physically-adjustable structures according to the perturbation matrix.
2. The method for automatically debugging a microwave filter based on response feature extraction according to claim 1, wherein extracting the current pole-zero of S11 and S21 and the gradient of the preset topology element pole-zero of the matrix according to the coupling elements of the coupling matrix specifically comprises:
And obtaining the current zero poles of the S11 and the S21 by calculating the matrix beam generalized eigenvalue, and calculating the preset topological element by the generalized eigenvector corresponding to the current zero pole of the S11.
3. The method for automatically debugging a microwave filter based on response feature extraction according to claim 1, wherein constructing a square loss function of the extracted zero poles and the preset zero poles and adding a constraint term of a preset topological element to construct an optimization target specifically comprises:
the following square loss function C 1 was constructed:
wherein z p is the target pole of S11 and S21, z r is the target zero of S11, z t is the target zero of S21, And (3) withIs a corresponding target value;
The optimization objective of the joining constraint term construct is:
C＝C1+μ∑|Mi,j|；
where μ represents a constant, |m i,j | represents the absolute value of the preset topology element.
4. The method for automatically debugging a microwave filter based on response feature extraction as claimed in claim 3, wherein said optimizing the optimization objective to obtain the perturbation matrix specifically comprises:
Calculating the derivative of the element M i,j of the topology (i, j) location with respect to the loss function C 1;
solving C=C 1+μ∑|Mi,j | through a quasi-Newton algorithm, solving an optimized coupling matrix M *, and then calculating a perturbation matrix delta M of the coupling matrix as
ΔM＝M*-M1。
5. The method for automatically debugging a microwave filter based on response feature extraction of claim 1, wherein the applying a debugging to a limited number of physically tunable structures according to the perturbation matrix comprises:
and adjusting a physical adjustable structure corresponding to the non-zero position in the perturbation matrix according to the perturbation value of the perturbation matrix.
6. The method for automatically debugging a microwave filter based on response feature extraction of claim 5, further comprising:
After the debugging is finished, judging whether the electric performance of the filter meets engineering indexes, if so, finishing the debugging, and if not, giving out the target value of the S parameter again to continue the debugging.
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| KR100563491B1 (en) * | 2003-11-11 | 2006-03-27 | 한국전자통신연구원 | Method for Multiple Passband Filter of Canonical Structure |
| CN109063374B (en) * | 2018-08-31 | 2021-07-09 | 中国地质大学（武汉） | Parameter optimization-based coupling matrix extraction method and device and storage device |
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